
library(stringr)
library(dplyr)
library(reshape)
library(ggplot2)
library(psych)

library(viridis)
library(scales)
library(rcartocolor)
library(effsize)
library(extrafont)

library(tidyverse)
library(magrittr)

library(apaTables)
library(MBESS)
library(xtable)
library(Hmisc)
library(modelsummary)

Attaching package: ‘dplyr’


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    filter, lag


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    intersect, setdiff, setequal, union


Attaching package: ‘reshape’


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Attaching package: ‘psych’


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    %+%, alpha


Loading required package: viridisLite


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Registering fonts with R

── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

✔ tibble  3.1.6     ✔ purrr   0.3.4
✔ tidyr   1.1.3     ✔ forcats 0.5.1
✔ readr   1.4.0     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ psych::%+%()        masks ggplot2::%+%()
✖ scales::alpha()     masks psych::alpha(), ggplot2::alpha()
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    format.pval, units


Attaching package: ‘modelsummary’


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sessionInfo()

R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] ja_JP.UTF-8/ja_JP.UTF-8/ja_JP.UTF-8/C/ja_JP.UTF-8/ja_JP.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] modelsummary_1.4.3 Hmisc_4.6-0        Formula_1.2-4      survival_3.2-7    
 [5] lattice_0.20-41    xtable_1.8-4       MBESS_4.9.2        apaTables_2.0.8   
 [9] magrittr_2.0.2     forcats_0.5.1      purrr_0.3.4        readr_1.4.0       
[13] tidyr_1.1.3        tibble_3.1.6       tidyverse_1.3.1    extrafont_0.19    
[17] effsize_0.8.1      rcartocolor_2.0.0  scales_1.1.1       viridis_0.5.1     
[21] viridisLite_0.4.0  psych_2.0.12       ggplot2_3.3.5      reshape_0.8.8     
[25] dplyr_1.0.7        stringr_1.4.0     

loaded via a namespace (and not attached):
 [1] nlme_3.1-149        fs_1.5.0            lubridate_1.7.10   
 [4] insight_0.19.7      RColorBrewer_1.1-2  httr_1.4.2         
 [7] repr_1.1.0          tools_4.0.3         backports_1.2.1    
[10] DT_0.17             utf8_1.2.2          R6_2.5.1           
[13] rpart_4.1-15        DBI_1.1.1           colorspace_2.0-2   
[16] nnet_7.3-14         withr_2.4.3         tidyselect_1.1.1   
[19] gridExtra_2.3       mnormt_2.0.2        compiler_4.0.3     
[22] extrafontdb_1.0     cli_3.1.1           rvest_1.0.0        
[25] htmlTable_2.4.1     xml2_1.3.2          checkmate_2.1.0    
[28] tables_0.9.17       pbdZMQ_0.3-4        digest_0.6.29      
[31] foreign_0.8-80      rmarkdown_2.9       base64enc_0.1-3    
[34] jpeg_0.1-9          pkgconfig_2.0.3     htmltools_0.5.0    
[37] dbplyr_2.1.1        htmlwidgets_1.5.3   rlang_1.0.1        
[40] readxl_1.3.1        rstudioapi_0.13     generics_0.1.2     
[43] jsonlite_1.7.2      interp_1.0-33       Matrix_1.2-18      
[46] Rcpp_1.0.9          IRkernel_1.1.1.9000 munsell_0.5.0      
[49] fansi_1.0.2         lifecycle_1.0.1     stringi_1.5.3      
[52] plyr_1.8.6          grid_4.0.3          parallel_4.0.3     
[55] crayon_1.4.2        deldir_1.0-6        IRdisplay_0.7.0    
[58] haven_2.3.1         splines_4.0.3       hms_1.1.0          
[61] tmvnsim_1.0-2       knitr_1.30          pillar_1.7.0       
[64] uuid_0.1-4          reprex_2.0.0        glue_1.6.1         
[67] evaluate_0.14       latticeExtra_0.6-30 data.table_1.13.6  
[70] modelr_0.1.8        vctrs_0.3.8         png_0.1-7          
[73] Rttf2pt1_1.3.12     cellranger_1.1.0    gtable_0.3.0       
[76] assertthat_0.2.1    xfun_0.24           broom_0.7.6        
[79] cluster_2.1.0       ellipsis_0.3.2


df<-read.csv("maindata.csv")


head(df)


colnames(df)


dim(df)


rep(c(1:100),50) %>% sort() -> NumID
df<-cbind(df, NumID)


CPalet<-c("#117733","#6699CC","#CF6677")
show_col(CPalet)

CPalet


df %>% 
group_by(NumID) %>% 
  summarise(Efficiency= mean(Fitness)) -> Efficiency

df1<-df[df$Trial==1,] 
df1chosen<-df1[c("Generation","ExpCondition","ChainID", "Age", "Gender", "ID")]


cbind(
Efficiency,df1chosen) -> EfficiencyDf


 EfficiencyDf

#head(Efficiency)
#cbind(Efficiency, PptGeneration,PptCondition, PptSessionID ) 
#cbind(Efficiency, PptGeneration,PptCondition, PptSessionID ) -> EfficiencyDf

#colnames(EfficiencyDf)<-c("ID","Efficiency","Generation","ExpCondition","SessionID")
#EfficiencyDf


EfficiencyDf$Generation - EfficiencyDf$Generation_backup


df %>% 
group_by(NumID) %>% 
  summarise(sum_choice = sum(Choice-1)) -> Exploration

df1<-df[df$Trial==1,]

PSessionID<-df1$ChainID
PGeneration<-df1$Generation
PCondition<-df1$ExpCondition
PGender<-df1$Gender



cbind(Exploration, PGeneration,PCondition, PSessionID, PGender) -> ExpDf
colnames(ExpDf)<-c("ID","Exploration","Generation","ExpCondition","SessionID","Gender")

head(ExpDf)


library(extrafont)


labeli <- as_labeller(c("I" = "Asocial",
                         "O" = "Unrepaid",
                         "V" = "Repaid",
                        "1"="1","2"="2","3"="3","4"="4","5"="5","6"="6","7"="7","8"="8","9"="9","10"="10","11"="11",
                        "12"="12","13"="13","14"="14",
                        "15"="15","16"="16","17"="17","18"="18","19"="19","20"="20"))

#my_x_title <- expression(paste("Trial (", italic("T"), ")"))
my_x_title <- "Trial"


options(repr.plot.width=10, repr.plot.height=5.2)
#options(repr.plot.width=20, repr.plot.height=20)
g<-ggplot(df[df$Generation == 1, ], aes(x=Trial, y= Choice-1, color = as.factor(ExpCondition))) + 
ylim(0,1)+
theme_bw()+geom_point()+geom_line(alpha = 1, size = 0.2)+facet_grid(as.factor(ChainID)~ExpCondition ,labeller = labeli)+
theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
      panel.grid.major.y = element_blank(),
      panel.grid.minor.y = element_blank()
     )+ xlab(my_x_title)+ 
theme(legend.position = "none")+
scale_color_manual(values = CPalet)
g

ggsave("FiguresRev/FigExplExpr.pdf", dpi = 220, device = "pdf", width = 10, height = 5.2)
ggsave("FiguresRev/FigExplExpr.eps", dpi = 220, device = "eps", width = 10, height = 5.2)
ggsave("FiguresRev/FigExplExpr.png", dpi = 220, device = "png", width = 10, height = 5.2)


DF<-ExpDf[,c(1,2,3,4)] 
head(DF)
DF %>% 
    group_by(Generation, ExpCondition) %>%
  summarise(mean = mean(Exploration), SE =　 sd(Exploration)/sqrt(20)) -> SmrDF

SmrDF$SE %>% round(.,3) -> SmrDF$SE

`summarise()` has grouped output by 'Generation'. You can override using the `.groups` argument.


#DF[(DF$Generation == 1)&(DF$ExpCondition== "I"),]$Exploration
DF[(DF$Generation == 1)&(DF$ExpCondition== "I"),]$Exploration　%>%   rank(., ties.method= "first") -> orderI1
orderI1<-length(orderI1)+1-orderI1
DF[(DF$Generation == 2)&(DF$ExpCondition== "I"),]$Exploration　%>%   rank(., ties.method= "first") -> orderI2
orderI2<-length(orderI2)+1-orderI2



#DF[(DF$Generation == 1)&(DF$ExpCondition== "O"),]$Exploration
DF[(DF$Generation == 1)&(DF$ExpCondition== "O"),]$Exploration %>%  rank(., ties.method= "first") -> orderO1
orderO1<-length(orderO1)+1-orderO1
DF[(DF$Generation == 2)&(DF$ExpCondition== "O"),]$Exploration %>%  rank(., ties.method= "first") -> orderO2
orderO2<-length(orderO2)+1-orderO2


#DF[(DF$Generation == 1)&(DF$ExpCondition== "V"),]$Exploration
DF[(DF$Generation == 1)&(DF$ExpCondition== "V"),]$Exploration %>%  rank(., ties.method= "first") -> orderV1
orderV1<-length(orderV1)+1-orderV1
DF[(DF$Generation == 2)&(DF$ExpCondition== "V"),]$Exploration %>%  rank(., ties.method= "first")-> orderV2
orderV2<-length(orderV2)+1-orderV2


Order<-df$ChainID
df<-mutate(df, Order)

df[(df$Generation == 1)&(df$ExpCondition== "I"),]$ChainID -> tempIDList
df[(df$Generation == 1)&(df$ExpCondition== "I"),]$Order<-orderI1[tempIDList] 

df[(df$Generation == 2)&(df$ExpCondition== "I"),]$ChainID -> tempIDList
df[(df$Generation == 2)&(df$ExpCondition== "I"),]$Order<-orderI2[tempIDList] 


df[(df$Generation == 1)&(df$ExpCondition== "O"),]$ChainID -> tempIDList
df[(df$Generation == 1)&(df$ExpCondition== "O"),]$Order<-orderO1[tempIDList] 

df[(df$Generation == 2)&(df$ExpCondition== "O"),]$ChainID -> tempIDList
df[(df$Generation == 2)&(df$ExpCondition== "O"),]$Order<-orderO2[tempIDList] 

df[(df$Generation == 1)&(df$ExpCondition== "V"),]$ChainID -> tempIDList
df[(df$Generation == 1)&(df$ExpCondition== "V"),]$Order<-orderV1[tempIDList] 

df[(df$Generation == 2)&(df$ExpCondition== "V"),]$ChainID -> tempIDList
df[(df$Generation == 2)&(df$ExpCondition== "V"),]$Order<-orderV2[tempIDList]


tempIDList[orderV1] %>% length()


df[(df$Generation == 2)&(df$ExpCondition== "V"),]$Order %>% length()


labeli <- as_labeller(c("I" = "Asocial",
                         "O" = "Unrepaid",
                         "V" = "Repaid",
                        "1"="1","2"="2","3"="3","4"="4","5"="5","6"="6","7"="7","8"="8","9"="9","10"="10","11"="11",
                        "12"="12","13"="13","14"="14",
                        "15"="15","16"="16","17"="17","18"="18","19"="19","20"="20"))

my_x_title <- expression("Trial")

options(repr.plot.width=10, repr.plot.height=5.2)
#options(repr.plot.width=20, repr.plot.height=20)
g<-ggplot(df[df$Generation == 1, ], aes(x=Trial, y= Choice-1, color = as.factor(ExpCondition))) + 
ylim(0,1)+
theme_bw()+geom_point()+geom_line(alpha = 1, size = 0.2)+facet_grid(as.factor(Order)~ExpCondition ,labeller = labeli)+
theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
      panel.grid.major.y = element_blank(),
      panel.grid.minor.y = element_blank()
     )+ xlab(my_x_title)+ 
theme(legend.position = "none")+
scale_color_manual(values = CPalet)
g

ggsave("FiguresRev/FigExplExpr.pdf", dpi = 220, device = "pdf", width = 10, height = 5.2)
ggsave("FiguresRev/FigExplExpr.eps", dpi = 220, device = "eps", width = 10, height = 5.2)
ggsave("FiguresRev/FigExplExpr.png", dpi = 220, device = "png", width = 10, height = 5.2)


DF<-ExpDf[,c(1,2,3,4)] 
head(DF)
DF %>% 
    group_by(Generation, ExpCondition) %>%
  summarise(mean = mean(Exploration), SE =　 sd(Exploration)/sqrt(20)) -> SmrDF

SmrDF$SE %>% round(.,3) -> SmrDF$SE 

SmrDF

`summarise()` has grouped output by 'Generation'. You can override using the `.groups` argument.


#ExpDf[(ExpDf$Generation == 1)&(ExpDf$ExpCondition!= "O"),] -> JASPVI
#ExpDf[(ExpDf$Generation == 1)&(ExpDf$ExpCondition!= "V"),] -> JASPOI
#write.csv(JASPVI, file="JASPVI.csv", sep=",")
#write.csv(JASPOI, file="JASPOI.csv", sep=",")


y_title <- expression(paste("Exploration trials (", italic(tau), ")"))
#taulabel<-"a"

taulabel<- expression(italic(tau)*"*")

options(repr.plot.width=6, repr.plot.height=3)

suppressMessages(
 suppressWarnings(
g <- ggplot(ExpDf[ExpDf$Generation ==1,], aes(x =as.factor(ExpCondition), y = Exploration,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) + 
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Unrepaid", "V" = "Repaid"))+
stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
 geom_segment(aes(x=0.6,xend=1.22,y=(Generation-1)*12,yend=(Generation-1)*12),linetype=2, color = "grey39", size = 0.4)+
 geom_segment(aes(x=1.6,xend=2.22,y=(Generation-1)*12,yend=(Generation-1)*12),linetype=2, color = "grey39", size = 0.4)+
 geom_segment(aes(x=2.6,xend=3.22,y=(Generation-1)*22,yend=(Generation-1)*22),linetype=2, color = "grey39", size = 0.4)+
annotate("text",x=1.3,y=0.01+12,label=taulabel) +
annotate("text",x=2.3,y=0.01+12,label=taulabel) +
annotate("text",x=3.3,y=0.01+22,label=taulabel) 
     ))

suppressMessages(
 suppressWarnings(g)
    )


 ggsave("Figures/MFreqExpo.pdf", dpi = 220, device = "pdf", width = 5, height =3)

Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”


y_title <- expression(paste("Mean exploration rate (", italic("W/T"), ")"))


labeli <- as_labeller(c("I" = "Asocial",
                         "O" = "Unnrepaid",
                         "V" = "Repaid",
                        "1"="First Generation","2"="Second Generation"))

options(repr.plot.width=5, repr.plot.height=3)
ggplot(SmrDF, aes(y=mean, x=ExpCondition,fill=ExpCondition))+
  geom_bar(stat = "identity", width = 0.4 , color = "black")+theme_bw()+
  geom_errorbar(aes(ymax = mean + SE, ymin = mean - SE), width = 0.1)+
                facet_grid(Generation~., labeller = labeli )+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Unrepaid", "V" = "Repaid"))


#ggsave("MExpRate.pdf", dpi = 220, device = "pdf", width = 5, height =3)


y_title <- expression(paste("Exploration trials (", italic(tau), ")"))
#taulabel<-"a"

taulabel<- expression(italic(tau)*"*")

options(repr.plot.width=6, repr.plot.height=3)

suppressMessages(
 suppressWarnings(
g <- ggplot(ExpDf[ExpDf$Generation ==1,], aes(x =as.factor(ExpCondition), y = Exploration,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) + 
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Unrepaid", "V" = "Repaid"))+
#stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
geom_boxplot(alpha=0.7, width =0.2, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
 geom_segment(aes(x=0.6,xend=1.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
 geom_segment(aes(x=1.6,xend=2.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
 geom_segment(aes(x=2.6,xend=3.22,y=22,yend=22),linetype=2, color = "grey39", size = 0.4)+
     stat_summary(fun= mean,  geom="point", size = 2.5, shape = 18 ,alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
              fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
              geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
annotate("text",x=1.3,y=0.01+12,label=taulabel) +
annotate("text",x=2.3,y=0.01+12,label=taulabel) +
annotate("text",x=3.3,y=0.01+22,label=taulabel) 
     ))

suppressMessages(
 suppressWarnings(g)
    )


 ggsave("Figures/MFreqExpoR1.pdf", dpi = 220, device = "pdf", width = 5, height =3)
 ggsave("Figures/MFreqExpoR1.png", dpi = 220, device = "png", width = 5, height =3)

Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”


y_title <- expression(paste("Exploration trials (", italic(tau), ")"))
#taulabel<-"a"

taulabel<- expression(italic(tau)*"*")

options(repr.plot.width=6, repr.plot.height=3)

suppressMessages(
 suppressWarnings(
g <- ggplot(ExpDf[ExpDf$Generation ==1,], aes(x =as.factor(ExpCondition), y = Exploration,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) + 
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Unrepaid", "V" = "Repaid"))+
#stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
geom_boxplot(alpha=0.7, width =0.2, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
 geom_segment(aes(x=0.6,xend=1.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
 geom_segment(aes(x=1.6,xend=2.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
 geom_segment(aes(x=2.6,xend=3.22,y=22,yend=22),linetype=2, color = "grey39", size = 0.4)+
      geom_segment(aes(x=1,xend=3,y=43,yend=43),linetype=1, color = "grey39", size = 0.2)+
     geom_segment(aes(x=1,xend=1,y=41,yend=43),linetype=1, color = "grey39", size = 0.2)+
          geom_segment(aes(x=3,xend=3,y=41,yend=43),linetype=1, color = "grey39", size = 0.2)+
      geom_segment(aes(x=1,xend=2,y=33,yend=33),linetype=1, color = "grey39", size = 0.2)+
     geom_segment(aes(x=1,xend=1,y=31,yend=33),linetype=1, color = "grey39", size = 0.2)+
          geom_segment(aes(x=2,xend=2,y=31,yend=33),linetype=1, color = "grey39", size = 0.2)+
     stat_summary(fun= mean,  geom="point", size = 2.5, shape = 18 ,alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
              fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
              geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
annotate("text",x=1.3,y=0.01+12,label=taulabel) +
annotate("text",x=2.3,y=0.01+12,label=taulabel) +
annotate("text",x=3.3,y=0.01+22,label=taulabel) +
     annotate("text",x=2,y=45,label="* (p = 0.010)") +
      annotate("text",x=1.5,y=34.5,label="NS (p = 0.661)") 
     ))

suppressMessages(
 suppressWarnings(g)
    )


 ggsave("Figures/MFreqExpoR1_sig.pdf", dpi = 220, device = "pdf", width = 5, height =3)
 ggsave("Figures/MFreqExpoR1_sig.png", dpi = 220, device = "png", width = 5, height =3)

Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”


extrafont::loadfonts()

y_title <- expression(paste("Exploration trials (", italic(tau), ")"))
#taulabel<-"a"

taulabel<- expression(italic(tau)*"*")

options(repr.plot.width=6, repr.plot.height=3)

suppressMessages(
 suppressWarnings(
g <- ggplot(ExpDf[ExpDf$Generation ==1,], aes(x =as.factor(ExpCondition), y = Exploration,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) + 
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Unrepaid", "V" = "Repaid"))+
#stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
geom_boxplot(alpha=0.7, width =0.2, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
 geom_segment(aes(x=0.6,xend=1.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
 geom_segment(aes(x=1.6,xend=2.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
 geom_segment(aes(x=2.6,xend=3.22,y=22,yend=22),linetype=2, color = "grey39", size = 0.4)+
      geom_segment(aes(x=1,xend=3,y=43,yend=43),linetype=1, color = "grey39", size = 0.2)+
     geom_segment(aes(x=1,xend=1,y=41,yend=43),linetype=1, color = "grey39", size = 0.2)+
          geom_segment(aes(x=3,xend=3,y=41,yend=43),linetype=1, color = "grey39", size = 0.2)+
      geom_segment(aes(x=1,xend=2,y=33,yend=33),linetype=1, color = "grey39", size = 0.2)+
     geom_segment(aes(x=1,xend=1,y=31,yend=33),linetype=1, color = "grey39", size = 0.2)+
          geom_segment(aes(x=2,xend=2,y=31,yend=33),linetype=1, color = "grey39", size = 0.2)+
     stat_summary(fun= mean,  geom="point", size = 2.5, shape = 18 ,alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
              fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
              geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
annotate("text",x=1.3,y=0.01+12,label=taulabel) +
annotate("text",x=2.3,y=0.01+12,label=taulabel) +
annotate("text",x=3.3,y=0.01+22,label=taulabel) +
     annotate("text",x=2,y=43,label="*", size = 5) +
      annotate("text",family = "Helvetica",x=1.5,y=34.5,label="NS", size = 2.5) 
     ))

suppressMessages(
 suppressWarnings(g)
    )


 ggsave("Figures/MFreqExpoR1_sig1.pdf", dpi = 220, device = "pdf", width = 5, height =3)
 ggsave("Figures/MFreqExpoR1_sig1.png", dpi = 220, device = "png", width = 5, height =3)

Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”
Warning message in is.na(x):
“ is.na() は型 'expression' のベクトル、リスト以外に適用されます ”


y_title <- expression(paste("Exploration trials (", italic(tau), ")"))
#taulabel<-"a"

taulabel<- expression(italic(tau)*"*")

options(repr.plot.width=6, repr.plot.height=6)

suppressMessages(
 suppressWarnings(
g <- ggplot(ExpDf, aes(x =as.factor(ExpCondition), y = Exploration,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) + 
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Unrepaid", "V" = "Repaid"))+
#stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
geom_boxplot(alpha=0.7, width =0.2, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
# geom_segment(aes(x=0.6,xend=1.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
# geom_segment(aes(x=1.6,xend=2.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
# geom_segment(aes(x=2.6,xend=3.22,y=22,yend=22),linetype=2, color = "grey39", size = 0.4)+
     stat_summary(fun= mean,  geom="point", size = 3, shape = 18 , alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
              fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
              geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
                facet_grid(Generation~., labeller = labeli )
     #+
#annotate("text",x=1.3,y=0.01+12,label=taulabel) +
#annotate("text",x=2.3,y=0.01+12,label=taulabel) +
#annotate("text",x=3.3,y=0.01+22,label=taulabel) 
 
 ))

suppressMessages(
 suppressWarnings(g)
    )


 ggsave("Figures/MFreqExpo2Gen.png", dpi = 220, device = "png", width = 5, height =3)

 ggsave("Figures/MFreqExpo2Gen.pdf", dpi = 220, device = "pdf", width = 5, height =3)


y_title <- expression(paste("Exploration trials (", italic(tau), ")"))
#taulabel<-"a"

taulabel<- expression(italic(tau)*"*")

options(repr.plot.width=6.5, repr.plot.height=3)

suppressMessages(
 suppressWarnings(
g <- ggplot(ExpDf, aes(x =as.factor(ExpCondition), y = Exploration,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) + 
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Unrepaid", "V" = "Repaid"))+
#stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
geom_boxplot(alpha=0.7, width =0.2, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
 geom_segment(aes(x=0.6,xend=1.22,y=(-1*Generation+2)*12,yend=(-1*Generation+2)*12),linetype=2, color = "grey39", size = 0.4)+
 geom_segment(aes(x=1.6,xend=2.22,y=(-1*Generation+2)*12,yend=(-1*Generation+2)*12),linetype=2, color = "grey39", size = 0.4)+
 geom_segment(aes(x=2.6,xend=3.22,y=(-1*Generation+2)*22,yend=(-1*Generation+2)*22),linetype=2, color = "grey39", size = 0.4)+
     stat_summary(fun= mean,  geom="point", size = 3, shape = 18 , alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
     stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
              fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
              geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
#annotate("text",x=1.3,y=0.01+(-1*Generation+2)*12,label=taulabel) +
#annotate("text",x=2.3,y=0.01+(-1*Generation+2)*12,label=taulabel) +
#annotate("text",x=3.3,y=0.01+(-1*Generation+2)*22,label=taulabel) +
                facet_grid(.~Generation, labeller = labeli )
     #+
#annotate("text",x=1.3,y=0.01+12,label=taulabel) +
#annotate("text",x=2.3,y=0.01+12,label=taulabel) +
#annotate("text",x=3.3,y=0.01+22,label=taulabel) 
 
 ))

suppressMessages(
 suppressWarnings(g)
    )

ggsave("Figures/MFreqExpo2Gen.png", dpi = 220, device = "png", width = 6.5, height =3)
ggsave("Figures/MFreqExpo2Gen.pdf", dpi = 220, device = "pdf", width = 6.5, height =3)


y_title <- expression(paste("Exploration trials (", italic(tau), ")"))
#taulabel<-"a"

taulabel<- expression(italic(tau)*"*")


options(repr.plot.width=9, repr.plot.height=3)

suppressMessages(
 suppressWarnings(
g <- ggplot(ExpDf, aes(x =as.factor(ExpCondition), y = Exploration,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) + 
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title)+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Unrepaid", "V" = "Repaid"))+
#stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
geom_boxplot(alpha=0.7, width =0.2, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
# geom_segment(aes(x=0.6,xend=1.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
# geom_segment(aes(x=1.6,xend=2.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
# geom_segment(aes(x=2.6,xend=3.22,y=22,yend=22),linetype=2, color = "grey39", size = 0.4)+
     stat_summary(fun= mean,  geom="point", size = 3, shape = 18 , alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
              fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
              geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
                facet_grid(.~Generation, labeller = labeli )
     #+
#annotate("text",x=1.3,y=0.01+12,label=taulabel) +
#annotate("text",x=2.3,y=0.01+12,label=taulabel) +
#annotate("text",x=3.3,y=0.01+22,label=taulabel) 
 
 ))

suppressMessages(
 suppressWarnings(g)
    )


ggsave("Figures/MFreqExpo2Gen.png", dpi = 220, device = "png", width = 6.5, height =3)
ggsave("Figures/MFreqExpo2Gen.pdf", dpi = 220, device = "pdf", width = 6.5, height =3)


"IND"
ExpDf[(ExpDf$Generation == 1)&(ExpDf$ExpCondition == "I"),]$Exploration -> IND_Expo
round(psych::describe( IND_Expo),2)

"OBL"
ExpDf[(ExpDf$Generation == 1)&(ExpDf$ExpCondition == "O"),]$Exploration -> OBL_Expo
round(psych::describe( OBL_Expo),2)

"VER"
ExpDf[(ExpDf$Generation == 1)&(ExpDf$ExpCondition == "V"),]$Exploration -> VER_Expo
round(psych::describe( VER_Expo),2)


t.test(OBL_Expo, IND_Expo)
effsize::cohen.d(OBL_Expo, IND_Expo)

"Non-Repaid SD"
round(sd(IND_Expo),2)
"Asocial SD"
round(sd(VER_Expo),2)

	Welch Two Sample t-test

data:  OBL_Expo and IND_Expo
t = -0.44228, df = 32.064, p-value = 0.6613
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -3.643344  2.343344
sample estimates:
mean of x mean of y 
    19.45     20.10

Cohen's d

d estimate: -0.139862 (negligible)
95 percent confidence interval:
     lower      upper 
-0.7808138  0.5010898


library(effsize)
t.test(VER_Expo, IND_Expo)
effsize::cohen.d(VER_Expo, IND_Expo)

"Repaid SD"
round(sd(IND_Expo),2)
"Asocial SD"
round(sd(VER_Expo),2)

	Welch Two Sample t-test

data:  VER_Expo and IND_Expo
t = 2.7485, df = 29.398, p-value = 0.01013
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 1.153453 7.846547
sample estimates:
mean of x mean of y 
     24.6      20.1

Cohen's d

d estimate: 0.8691655 (large)
95 percent confidence interval:
    lower     upper 
0.1994516 1.5388794


#"IND"
#ExpDf[(ExpDf$Generation == 2)&(ExpDf$ExpCondition == "I"),]$Exploration -> IND_Expo2nd
#round(psych::describe( IND_Expo2nd),2)

"OBL"
ExpDf[(ExpDf$Generation == 2)&(ExpDf$ExpCondition == "O"),]$Exploration -> OBL_Expo2nd
round(psych::describe( OBL_Expo2nd),2)

"VER"
ExpDf[(ExpDf$Generation == 2)&(ExpDf$ExpCondition == "V"),]$Exploration -> VER_Expo2nd
round(psych::describe( VER_Expo2nd),2)


t.test(OBL_Expo2nd, OBL_Expo, paired=TRUE )

	Paired t-test

data:  OBL_Expo2nd and OBL_Expo
t = -4.169, df = 19, p-value = 0.0005209
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -12.467002  -4.132998
sample estimates:
mean of the differences 
                   -8.3


effsize::cohen.d(OBL_Expo2nd, OBL_Expo, paired=TRUE)

Cohen's d

d estimate: -1.525274 (large)
95 percent confidence interval:
     lower      upper 
-2.6146204 -0.4359282


t.test(VER_Expo2nd, VER_Expo, paired=TRUE )

	Paired t-test

data:  VER_Expo2nd and VER_Expo
t = -5.7703, df = 19, p-value = 1.467e-05
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -19.010015  -8.889985
sample estimates:
mean of the differences 
                 -13.95


effsize::cohen.d(VER_Expo2nd, VER_Expo, paired=TRUE)

Cohen's d

d estimate: -2.070038 (large)
95 percent confidence interval:
     lower      upper 
-3.3574460 -0.7826294


t.test(OBL_Expo2nd, VER_Expo2nd, paired=FALSE )

	Welch Two Sample t-test

data:  OBL_Expo2nd and VER_Expo2nd
t = 0.25326, df = 35.368, p-value = 0.8015
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -3.506432  4.506432
sample estimates:
mean of x mean of y 
    11.15     10.65


effsize::cohen.d(OBL_Expo2nd, VER_Expo2nd)

Cohen's d

d estimate: 0.0800884 (negligible)
95 percent confidence interval:
     lower      upper 
-0.5603378  0.7205146


"OBL2"
ExpDf[(ExpDf$Generation == 2)&(ExpDf$ExpCondition == "O"),]$Exploration -> OBL_Expo2
round(psych::describe( OBL_Expo2),2)

"VER2"
ExpDf[(ExpDf$Generation == 2)&(ExpDf$ExpCondition == "V"),]$Exploration -> VER_Expo2
round(psych::describe( VER_Expo2),2)


library(effsize)
t.test(OBL_Expo2, VER_Expo2)
effsize::cohen.d(OBL_Expo2, VER_Expo2)

"Repaid SD"
round(sd(OBL_Expo2),2)
"Asocial SD"
round(sd(VER_Expo2),2)

	Welch Two Sample t-test

data:  OBL_Expo2 and VER_Expo2
t = 0.25326, df = 35.368, p-value = 0.8015
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -3.506432  4.506432
sample estimates:
mean of x mean of y 
    11.15     10.65

Cohen's d

d estimate: 0.0800884 (negligible)
95 percent confidence interval:
     lower      upper 
-0.5603378  0.7205146

df


## Detecting Over Explorer


LengthOptimal <- 70
WidthOptimal <-48
ThicknessOptimal<-11


OverExpLength<-(df$Length > LengthOptimal)+0 
OverExpWidth<-(df$Width > WidthOptimal)+0 
OverExpThickness<-(df$Thickness < ThicknessOptimal)+0

df<-dplyr::mutate(df, OverExpLength)
df<-dplyr::mutate(df, OverExpWidth)
df<-dplyr::mutate(df, OverExpThickness)


df[(df$Length > LengthOptimal)&(df$Generation == 1),]$NumID %>% unique() -> OverLengthIDG1
df[(df$Width > WidthOptimal)&(df$Generation == 1),]$NumID %>% unique() -> OverWidthIDG1
df[(df$Thickness < ThicknessOptimal)&(df$Generation == 1),]$NumID %>% unique() -> OverThicknessIDG1

df[(df$Length > LengthOptimal)&(df$Generation == 2),]$NumID %>% unique() -> OverLengthIDG2
df[(df$Width > WidthOptimal)&(df$Generation == 2),]$NumID %>% unique() -> OverWidthIDG2
df[(df$Thickness < ThicknessOptimal)&(df$Generation == 2),]$NumID %>% unique() -> OverThicknessIDG2

"L, G1"
OverLengthIDG1
length(OverLengthIDG1)
"W, G1"
OverWidthIDG1
length(OverWidthIDG1)
"T, G1"
OverThicknessIDG1
length(OverThicknessIDG1)

"Total, G1"
c(OverLengthIDG1, OverWidthIDG1, OverThicknessIDG1) %>%  unique() 
c(OverLengthIDG1, OverWidthIDG1, OverThicknessIDG1) %>%  unique()  %>% length()


OverLengthIDG2
OverWidthIDG2
OverThicknessIDG2


"Geneartion1"
"L,W,T"
df[(df$Length > LengthOptimal)&(df$Generation == 1),]$ID %>% unique() ->OLIDG1
df[(df$Width > WidthOptimal)&(df$Generation == 1),]$ID %>% unique() ->OWIDG1
df[(df$Thickness < ThicknessOptimal)&(df$Generation == 1),]$ID %>% unique() -> OTIDG1

OLIDG1
OWIDG1
OTIDG1
c(OLIDG1,OWIDG1,OTIDG1)%>% unique() 　-> AllIDG1
#c(OLIDG1,OWIDG1,OTIDG1)%>% unique() %>% length


OverIndNG1 <- str_detect(AllIDG1, pattern="I") %>% sum()
OverOblNG1 <- str_detect(AllIDG1, pattern="O") %>% sum()
OverVerNG1 <- str_detect(AllIDG1, pattern="V") %>% sum()


OverExpSummaryG1<-c(OverIndNG1 , OverOblNG1, OverVerNG1)
OverExpSummaryG1



"Geneartion2"
"L,W,T"
df[(df$Length > LengthOptimal)&(df$Generation == 2),]$ID %>% unique() ->OLIDG2
df[(df$Width > WidthOptimal)&(df$Generation == 2),]$ID %>% unique() ->OWIDG2
df[(df$Thickness < ThicknessOptimal)&(df$Generation == 2),]$ID %>% unique() -> OTIDG2

OLIDG2
OWIDG2
OTIDG2
c(OLIDG2,OWIDG2,OTIDG2)%>% unique() 　-> AllIDG2
#c(OLIDG2,OWIDG2,OTIDG2)%>% unique() %>% length


OverIndNG2<- str_detect(AllIDG2, pattern="I") %>% sum()
OverOblNG2 <- str_detect(AllIDG2, pattern="O") %>% sum()
OverVerNG2 <- str_detect(AllIDG2, pattern="V") %>% sum()


OverExpSummaryG2<-c(OverIndNG2 , OverOblNG2, OverVerNG2)
OverExpSummaryG2


rbind(OverExpSummaryG1, OverExpSummaryG2) %>% data.frame() -> OverExpSummary
colnames(OverExpSummary)<-c("asocial","unrepaid","repaid")
rownames(OverExpSummary)<-c("G1","G2")
OverExpSummary
OverExpSummary/20


OverExpLength<-(df$Length > LengthOptimal)+0 
OverExpWidth<-(df$Width > WidthOptimal)+0 
OverExpThickness<-(df$Thickness < ThicknessOptimal)+0

df


df[(df$Length > LengthOptimal)|(df$Generation==1) ,]


labeli <- as_labeller(c("I" = "Asocial",
                         "O" = "Unnrepaid",
                         "V" = "Repaid",
                        "1"="First Generation","2"="Second Generation"))

options(repr.plot.width=10, repr.plot.height=10)
ggplot(df, aes(x=Trial,y=Length, group = as.factor(ID), color = as.factor(ChainID))) + 
geom_point(alpha =0.5)+  geom_line(alpha=0.6)+
geom_hline(yintercept = 70, size = 0.6, linetype = 1) +
ylim(0,100)+
theme_bw()+
guides(color=guide_legend(title="ID"))+theme(legend.position = "none")+
theme(text = element_text(size = 16))+
facet_grid(ExpCondition ~ Generation,labeller = labeli) -> Lplot
Lplot


labeli <- as_labeller(c("I" = "Asocial",
                         "O" = "Unrepaid",
                         "V" = "Repaid",
                        "1"="First Generation","2"="Second Generation"))

options(repr.plot.width=12, repr.plot.height=12)
ggplot(df, aes(x=Trial,y=Length, group = as.factor(ID), color = as.factor(OverExpLength))) +  
geom_point(alpha =0.5)+  geom_line(alpha=0.5)+
guides(color=guide_legend(title="Overexploration"))+
geom_hline(yintercept = 70, size = 0.6, linetype = 1) +
ylim(0,100)+
theme_bw()+
theme(text = element_text(size = 16))+
facet_grid(ExpCondition ~ Generation,labeller = labeli)


options(repr.plot.width=10, repr.plot.height=10)
ggplot(df, aes(x=Trial,y=Width, group = as.factor(ID), color = as.factor(ChainID), alpha = 0.2)) + 
geom_point(alpha =0.5)+  geom_line(alpha=0.6)+
geom_hline(yintercept = 48, size = 0.6, linetype = 1) +
ylim(0,100)+
theme_bw()+
theme(text = element_text(size = 16))+
guides(color=guide_legend(title="ID"))+theme(legend.position = "none")+
facet_grid(ExpCondition ~ Generation,labeller = labeli) -> Wplot
Wplot


options(repr.plot.width=12, repr.plot.height=12)
ggplot(df, aes(x=Trial,y=Width, group = as.factor(ID), color = as.factor(OverExpWidth), alpha = 0.2)) +
geom_point(alpha =0.5)+  geom_line(alpha=0.5)+
guides(color=guide_legend(title="Overexploration"))+
geom_hline(yintercept = 48, size = 0.6, linetype = 1) +
ylim(0,100)+
theme_bw()+
theme(text = element_text(size = 16))+
facet_grid(ExpCondition ~ Generation,labeller = labeli)


options(repr.plot.width=10, repr.plot.height=10)
ggplot(df, aes(x=Trial,y=Thickness, group = as.factor(ID), color = as.factor(ChainID))) +
geom_point(alpha =0.5)+  geom_line(alpha=0.6)+
geom_hline(yintercept = 11, size = 0.6, linetype = 1) +
ylim(0,100)+
theme_bw()+
theme(text = element_text(size = 16))+
guides(color=guide_legend(title="ID"))+theme(legend.position = "none")+
facet_grid(ExpCondition ~ Generation,labeller = labeli) -> Tplot
Tplot


options(repr.plot.width=12, repr.plot.height=12)
ggplot(df, aes(x=Trial,y=Thickness, group = as.factor(ID), color = as.factor(OverExpThickness), alpha = 0.2)) +  
geom_point(alpha =0.5)+  geom_line(alpha=0.5)+
guides(color=guide_legend(title="Overexploration"))+
geom_hline(yintercept = 11, size = 0.6, linetype = 1) +
ylim(0,100)+
theme_bw()+
theme(text = element_text(size = 16))+
facet_grid(ExpCondition ~ Generation,labeller = labeli)


options(repr.plot.width=12, repr.plot.height=12)
y_title <- expression("Cumulative payoff")
x_title <- expression("Trial")
labeli <- as_labeller(c("I" = "Asocial",
                         "O" = "Nonrepaid",
                         "V" = "Repaid",
                        "1"="First Geneartion",
                        "2"="Second Generation"))

#options(repr.plot.width=6., repr.plot.height=6.5)
df %>%  ggplot(aes(x=Trial,y=as.numeric(EarnedMoney/2)))+
geom_line(aes(group =ChainID, color = as.factor(ChainID)), alpha = 0.3)+
geom_point(aes(group =ChainID, color = as.factor(ChainID)), alpha = 0.5)+  
facet_grid(ExpCondition ~ Generation, labeller=labeli ) +
                      theme_bw() +theme(legend.position = "none")+
stat_summary(fun="mean", geom="line", alpha = 1, size = 1.3) +
stat_summary(fun="mean", geom="point", alpha = 1, size = 1.6)+
ylab(y_title)+xlab(x_title)+theme(text = element_text(size = 16))+
stat_summary(fun = mean,
               fun.min = function(x) mean(x) - sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               alpha = 0.8,  geom = "linerange", size =0.5)+geom_hline(yintercept = 364, size = 0.4, linetype = 5)  -> CPayoffG
CPayoffG


library(gridExtra)
options(repr.plot.width=26, repr.plot.height=12)
grid.arrange(Lplot, Wplot, Tplot, nrow = 1) -> LWTg


LWTg
ggsave("Figures/LWTshift.pdf",LWTg, dpi = 220, device = "pdf", width = 26, height = 12)

Attaching package: ‘gridExtra’


The following object is masked from ‘package:dplyr’:

    combine

TableGrob (1 x 3) "arrange": 3 grobs
  z     cells    name           grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (1-1,2-2) arrange gtable[layout]
3 3 (1-1,3-3) arrange gtable[layout]


library(gridExtra)
options(repr.plot.width=18, repr.plot.height=18)






layout <- rbind(c(1, 2),
                 c(3, 4))
grid.arrange(Lplot, Wplot, Tplot, CPayoffG, layout_matrix =layout) -> LWTPayoffgraph
ggsave("Figures/LWTshiftPayoff.pdf", LWTPayoffgraph, dpi = 220, device = "pdf", width = 18, height = 18)


y_title <- expression("Technological efficiency")
x_title <- expression("Trial")
labeli <- as_labeller(c("I" = "Asocial",
                         "O" = "Unrepaid",
                         "V" = "Repaid",
                        "1"="First Geneartion",
                        "2"="Second Generation"))

options(repr.plot.width=6., repr.plot.height=6.5)
df %>%  ggplot(aes(x=Trial,y=as.numeric(Fitness)))+
geom_line(aes(group =ChainID, color = as.factor(ChainID)), alpha = 0.3)+
geom_point(aes(group =ChainID, color = as.factor(ChainID)),size = 0.7, alpha = 0.5)+  
facet_grid(ExpCondition ~ Generation, labeller=labeli ) +
                      theme_bw() +theme(legend.position = "none")+
stat_summary(fun="mean", geom="line", alpha = 1, size = 0.5) +
stat_summary(fun="mean", geom="point", alpha = 1, size = 0.9)+
ylab(y_title)+xlab(x_title)+
stat_summary(fun = mean,
               fun.min = function(x) mean(x) - sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               alpha = 0.8,  geom = "linerange", size =0.5)
ggsave("MEfficienct.pdf", dpi = 220, device = "pdf", width = 6, height = 6.5)
ggsave("MEfficienct.png", dpi = 220, device = "png", width = 6, height = 6.5)


options(repr.plot.width=6, repr.plot.height=3)
g <- ggplot(EfficiencyDf[EfficiencyDf$Generation ==1,], aes(x =as.factor(ExpCondition), y = Efficiency,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) +
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA), ylim=c(400,800))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Nonrepaid", "V" = "Repaid"))+
stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))


g


options(repr.plot.width=9, repr.plot.height=3)
g <- ggplot(EfficiencyDf, aes(x =as.factor(ExpCondition), y = Efficiency,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) +
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Unrepaid", "V" = "Repaid"))+
stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
                facet_grid(.~Generation, labeller = labeli )


g


y_title <- expression(paste("Exploration trials (", italic(tau), ")"))
#taulabel<-"a"

taulabel<- expression(italic(tau)*"*")

options(repr.plot.width=6, repr.plot.height=6)

suppressMessages(
 suppressWarnings(
g <- ggplot(ExpDf, aes(x =as.factor(ExpCondition), y = Exploration,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) + 
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Unrepaid", "V" = "Repaid"))+
#stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
geom_boxplot(alpha=0.7, width =0.2, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
# geom_segment(aes(x=0.6,xend=1.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
# geom_segment(aes(x=1.6,xend=2.22,y=12,yend=12),linetype=2, color = "grey39", size = 0.4)+
# geom_segment(aes(x=2.6,xend=3.22,y=22,yend=22),linetype=2, color = "grey39", size = 0.4)+
     stat_summary(fun= mean,  geom="point", size = 3, shape = 18 , alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
              fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
              geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
                facet_grid(Generation~., labeller = labeli )
     #+
#annotate("text",x=1.3,y=0.01+12,label=taulabel) +
#annotate("text",x=2.3,y=0.01+12,label=taulabel) +
#annotate("text",x=3.3,y=0.01+22,label=taulabel) 
 
 ))

suppressMessages(
 suppressWarnings(g)
    )


OblOffline<-df[(df$Generation == 1)&(df$ExpCondition == "O"), ]$Fitness 
VerOffline<-df[(df$Generation == 1)&(df$ExpCondition == "V"), ]$Fitness 
IndOffline<-df[(df$Generation == 1)&(df$ExpCondition == "I"), ]$Fitness


OblOfflineF<-df[(df$Generation == 1)&(df$ExpCondition == "O")&(df$Trial == 50), ]$Fitness 
VerOfflineF<-df[(df$Generation == 1)&(df$ExpCondition == "V")&(df$Trial == 50), ]$Fitness 
IndOfflineF<-df[(df$Generation == 1)&(df$ExpCondition == "I")&(df$Trial == 50), ]$Fitness 


OblOfflineF2<-df[(df$Generation == 2)&(df$ExpCondition == "O")&(df$Trial == 50), ]$Fitness 
VerOfflineF2<-df[(df$Generation == 2)&(df$ExpCondition == "V")&(df$Trial == 50), ]$Fitness


"Ind"
psych::describe(IndOfflineF) %>% round(.,2)
"Obl"
psych::describe(OblOfflineF)%>% round(.,2)
"Ver"
psych::describe(VerOfflineF)%>% round(.,2)


options(repr.plot.width=9, repr.plot.height=4)
g <- ggplot(df[df$Trial == 50,], aes(x =as.factor(ExpCondition), y =Fitness,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) +
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA),ylim=c(400,1000))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Nonrepaid", "V" = "Repaid"))+
stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
                facet_grid(.~Generation,labeller = labeli)


g


options(repr.plot.width=9, repr.plot.height=4)
g <- ggplot(df[df$Trial == 50,], aes(x =as.factor(ExpCondition), y =Fitness,fill =as.factor(ExpCondition))) +
  ggdist::stat_slab( aes(x =as.numeric(as.factor(ExpCondition))+0.1),
    alpha = 0.9,
    width = .24, 
    .width = c(0.00002, .92), 
  ) +
geom_jitter(size = 0.7,height=0, width =0.05, alpha = 0.7,aes(x = as.factor(ExpCondition), color =as.factor(ExpCondition))) +
coord_cartesian(xlim = c(1.0, NA),ylim=c(0,1000))+theme_bw()+
scale_color_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unrepaid","Repaid"))+
scale_fill_manual(values = CPalet, name = "Condition",labels = c("Asocial", "Unnrepaid","Repaid"))+
ylab(y_title )+xlab("Experimental Conition")+
scale_x_discrete("Condition", labels = c("I" = "Asocial","O" = "Nonrepaid", "V" = "Repaid"))+
stat_summary(fun= mean,  geom="bar", alpha = 1, width = 0.2, color = "black",aes(x = as.numeric(as.factor(ExpCondition))-0.2)) +
stat_summary(fun.min = function(x) mean(x) -sd(x)/sqrt(length(x)), 
               fun.max = function(x) mean(x) + sd(x)/sqrt(length(x)), 
               geom = "errorbar", width =0.05, aes(x = as.numeric(as.factor(ExpCondition))-0.2))+
                facet_grid(.~Generation,labeller = labeli)


g


library(exactRankTests)

 Package ‘exactRankTests’ is no longer under development.
 Please consider using package ‘coin’ instead.


t.test(VerOfflineF, IndOfflineF)
t.test(log(VerOfflineF), log(IndOfflineF))
wilcox.exact(VerOfflineF, IndOfflineF,paired=F)
effsize::cohen.d(VerOfflineF, IndOfflineF)

	Welch Two Sample t-test

data:  VerOfflineF and IndOfflineF
t = 0.66478, df = 33.171, p-value = 0.5108
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -38.00408  74.90408
sample estimates:
mean of x mean of y 
   838.70    820.25

	Welch Two Sample t-test

data:  log(VerOfflineF) and log(IndOfflineF)
t = 0.51941, df = 33.233, p-value = 0.6069
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.05271388  0.08886949
sample estimates:
mean of x mean of y 
 6.724187  6.706109

	Exact Wilcoxon rank sum test

data:  VerOfflineF and IndOfflineF
W = 233.5, p-value = 0.3724
alternative hypothesis: true mu is not equal to 0

Cohen's d

d estimate: 0.2102214 (small)
95 percent confidence interval:
     lower      upper 
-0.4317140  0.8521568


sd(VerOfflineF)
sd(IndOfflineF)


library(exactRankTests)

t.test(OblOfflineF, IndOfflineF)
t.test(log(OblOfflineF), log(IndOfflineF))
wilcox.exact(OblOfflineF, IndOfflineF,paired=F)
effsize::cohen.d(OblOfflineF, IndOfflineF)

	Welch Two Sample t-test

data:  OblOfflineF and IndOfflineF
t = -0.98299, df = 30.393, p-value = 0.3334
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -93.37118  32.67118
sample estimates:
mean of x mean of y 
   789.90    820.25

	Welch Two Sample t-test

data:  log(OblOfflineF) and log(IndOfflineF)
t = -1.0998, df = 28.342, p-value = 0.2807
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.13381614  0.04028977
sample estimates:
mean of x mean of y 
 6.659346  6.706109

	Exact Wilcoxon rank sum test

data:  OblOfflineF and IndOfflineF
W = 176.5, p-value = 0.5334
alternative hypothesis: true mu is not equal to 0

Cohen's d

d estimate: -0.3108495 (small)
95 percent confidence interval:
     lower      upper 
-0.9548737  0.3331747


sd(OblOfflineF)


t.test(VerOfflineF2, OblOfflineF2)
t.test(log(VerOfflineF2), log(OblOfflineF2))
wilcox.exact(VerOfflineF2, OblOfflineF2,paired=F)
effsize::cohen.d(VerOfflineF2, OblOfflineF2)

	Welch Two Sample t-test

data:  VerOfflineF2 and OblOfflineF2
t = 0.76869, df = 37.976, p-value = 0.4468
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -30.3853  67.5853
sample estimates:
mean of x mean of y 
   897.85    879.25

	Welch Two Sample t-test

data:  log(VerOfflineF2) and log(OblOfflineF2)
t = 0.75387, df = 37.953, p-value = 0.4556
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -0.03576342  0.07820096
sample estimates:
mean of x mean of y 
 6.796465  6.775246

	Exact Wilcoxon rank sum test

data:  VerOfflineF2 and OblOfflineF2
W = 232, p-value = 0.3945
alternative hypothesis: true mu is not equal to 0

Cohen's d

d estimate: 0.2430811 (small)
95 percent confidence interval:
     lower      upper 
-0.3994484  0.8856105


t.test(OblOfflineF2-OblOfflineF)
#cohen.d(OblOfflineF2-OblOfflineF)

OblCCE<-OblOfflineF2-OblOfflineF

	One Sample t-test

data:  OblOfflineF2 - OblOfflineF
t = 4.3618, df = 19, p-value = 0.0003357
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
  46.47505 132.22495
sample estimates:
mean of x 
    89.35


t.test(VerOfflineF2-VerOfflineF)
#cohen.d(VerOfflineF2-VerOfflineF)


VerCCE<-VerOfflineF2-VerOfflineF

	One Sample t-test

data:  VerOfflineF2 - VerOfflineF
t = 3.4751, df = 19, p-value = 0.002535
alternative hypothesis: true mean is not equal to 0
95 percent confidence interval:
 23.52447 94.77553
sample estimates:
mean of x 
    59.15


t.test(VerCCE,OblCCE)

	Welch Two Sample t-test

data:  VerCCE and OblCCE
t = -1.1339, df = 36.767, p-value = 0.2642
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
 -84.17601  23.77601
sample estimates:
mean of x mean of y 
    59.15     89.35


datad<-read.csv("PostQuestionData.csv")


colnames(datad)


datad %>% group_by(., condition, generation) %>% 
dplyr::summarize(, n = n(), Str_turn= 2-mean(Str_turn),  Str_switch= 2-mean(Str_switch))


datad %>% group_by(., condition, generation) %>% 
dplyr::summarize(, n = n(), Str_turn= (2-mean(Str_turn))*20,  Str_switch= (2-mean(Str_switch))*20)

`summarise()` has grouped output by 'condition'. You can override using the `.groups` argument.

`summarise()` has grouped output by 'condition'. You can override using the `.groups` argument.


datad %>% group_by(., condition, generation) %>% 
dplyr::summarize(, n = n(), Mingore_cost=round(mean (ignore_cost, na.rm = TRUE),2),
                SDingore_cost=round(sd(ignore_cost, na.rm = TRUE),2),
                 Moverspent=mean(overspent), 
                 SDoverspent=sd(overspent)%>%round(2), 
                 Mmorespent=mean(more_spent), 
                 SDmorespent=sd(more_spent)%>%round(2), 
                 Mmaximize=mean(maxim), 
                 SDmaximize=sd(maxim)%>%round(2))
                 


datad %>% group_by(., condition, generation) %>% 
dplyr::summarize(, n = n(), 
                 MFutureThink=mean(FutureThink),
                 SFutureThink=sd(FutureThink)%>%round(2), 
                 MAncestor=mean(Ancestor),
                 SAncestor=sd(Ancestor)%>%round(2),
                 MReturn=mean(Return),
                 SReturn=sd(Return)%>%round(2))

`summarise()` has grouped output by 'condition'. You can override using the `.groups` argument.

`summarise()` has grouped output by 'condition'. You can override using the `.groups` argument.


PROSOC_KEY<-c(3,2,1,3,2,1,1,3,2)
PROSELF_KEY<-c(2,1,3,2,1,3,2,1,3)
COMP_KEY    <-c(1,3,2,1,3,2,3,2,1)


SVO<-datad[,(dim(datad)[2]-8):dim(datad)[2]]
PSocScore<-apply(SVO==matrix(PROSOC_KEY,dim(SVO)[1],dim(SVO)[2],T) ,1, sum)
PSelfScore<-apply(SVO==matrix(PROSELF_KEY,dim(SVO)[1],dim(SVO)[2],T) ,1, sum)
CompScore<-apply(SVO==matrix(COMP_KEY,dim(SVO)[1],dim(SVO)[2],T) ,1, sum)


datadp<-mutate(datad, PSocScore,PSelfScore,CompScore)


#mean(datadp[(datadp$condition=="I")&(datadp$generation==1),]$PSocScore)
#mean(datadp[(datadp$condition=="O")&(datadp$generation==1),]$PSocScore)
#mean(datadp[(datadp$condition=="V")&(datadp$generation==1),]$PSocScore)
#mean(datadp[(datadp$condition=="O")&(datadp$generation==2),]$PSocScore)
#mean(datadp[(datadp$condition=="V")&(datadp$generation==2),]$PSocScore)


SVO_cat<-numeric(dim(datad)[1])
SVO_cat[PSocScore > 5.9]<-"C"
SVO_cat[PSocScore < 5.9]<-"N"
SVO_cat[9-PSocScore > 5.9]<-"D"
#SVO_cat

IfSocial<-numeric(dim(datad)[1])
IfSocial[PSocScore > 5.9]<-1

datadp<-mutate(datadp, SVO_cat,IfSocial)


datadp %>% group_by(., condition, generation) %>% 
dplyr::summarize(, n = n(), psoc_m = mean(PSocScore), psoc_sd = round(sd(PSocScore), 2),
          pself_m = mean(PSelfScore), pself_sd = round(sd(PSelfScore),2), 
          comp_m = mean(CompScore), comp_sd = round(sd(CompScore),2))　-> table_svo

table_svo

`summarise()` has grouped output by 'condition'. You can override using the `.groups` argument.


#latex(table_svo, file = "table_svo_dexc.tex")


(table(datadp[datadp$generation ==1,]$condition, datadp[datadp$generation ==1,]$SVO_cat)) ->G1SVO
(G1SVO)
(G1SVO)/20

   
     C  D  N
  I 10  9  1
  O 11  8  1
  V  9  9  2

   
       C    D    N
  I 0.50 0.45 0.05
  O 0.55 0.40 0.05
  V 0.45 0.45 0.10


(table(datadp[datadp$generation ==2,]$condition, datadp[datadp$generation ==2,]$SVO_cat)) ->G2SVO
(G2SVO)
(G2SVO)/20

   
     C  D  N
  O 10 10  0
  V  9 10  1

   
       C    D    N
  O 0.50 0.50 0.00
  V 0.45 0.50 0.05


(table(datadp[datadp$generation ==1,]$condition, datadp[datadp$generation ==1,]$IfSocial))

   
     0  1
  I 10 10
  O  9 11
  V 11  9


(table(datadp[datadp$generation ==1,]$condition, datadp[datadp$generation ==1,]$IfSocial)) ->G1IfSocial
G1IfSocial
G1IfSocial/20

   
     0  1
  I 10 10
  O  9 11
  V 11  9

   
       0    1
  I 0.50 0.50
  O 0.45 0.55
  V 0.55 0.45


ExpDfSVO<-mutate(ExpDf, SVO_cat, IfSocial,PSocScore)
filter(ExpDfSVO, Generation == 1) -> ExpDfSVOG1


head(ExpDfSVO)


Gaus_cat<-lm(data = ExpDfSVOG1, Exploration~ExpCondition+IfSocial)
summary(Gaus_cat)
AIC(Gaus_cat)

Call:
lm(formula = Exploration ~ ExpCondition + IfSocial, data = ExpDfSVOG1)

Residuals:
     Min       1Q   Median       3Q      Max 
-12.3287  -2.8776  -0.3287   3.8656  12.1780 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    20.3466     1.3821  14.721   <2e-16 ***
ExpConditionO  -0.6253     1.6927  -0.369   0.7132    
ExpConditionV   4.4753     1.6927   2.644   0.0106 *  
IfSocial       -0.4933     1.3856  -0.356   0.7232    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.348 on 56 degrees of freedom
Multiple R-squared:  0.1657,	Adjusted R-squared:  0.1211 
F-statistic: 3.709 on 3 and 56 DF,  p-value: 0.01666


apa.reg.table(Gaus_cat)


Regression results using Exploration as the criterion
 

     Predictor       b       b_95%_CI sr2  sr2_95%_CI             Fit
   (Intercept) 20.35** [17.58, 23.12]                                
 ExpConditionO   -0.63  [-4.02, 2.77] .00 [-.02, .02]                
 ExpConditionV   4.48*   [1.08, 7.87] .10 [-.04, .25]                
      IfSocial   -0.49  [-3.27, 2.28] .00 [-.02, .02]                
                                                           R2 = .166*
                                                      95% CI[.01,.31]
                                                                     

Note. A significant b-weight indicates the semi-partial correlation is also significant.
b represents unstandardized regression weights. 
sr2 represents the semi-partial correlation squared.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.


Gaus_cat_scale<-lm(data = ExpDfSVOG1, scale(Exploration)~ExpCondition+IfSocial)
summary(Gaus_cat_scale)
AIC(Gaus_cat_scale)

Call:
lm(formula = scale(Exploration) ~ ExpCondition + IfSocial, data = ExpDfSVOG1)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.16108 -0.50442 -0.05762  0.67761  2.13467 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)  
(Intercept)   -0.18172    0.24227  -0.750   0.4564  
ExpConditionO -0.10961    0.29672  -0.369   0.7132  
ExpConditionV  0.78448    0.29672   2.644   0.0106 *
IfSocial      -0.08647    0.24288  -0.356   0.7232  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9375 on 56 degrees of freedom
Multiple R-squared:  0.1657,	Adjusted R-squared:  0.1211 
F-statistic: 3.709 on 3 and 56 DF,  p-value: 0.01666


options(digits=2)
summary(Gaus_cat_scale)$coefficients


apa.reg.table(Gaus_cat_scale)


Regression results using scale(Exploration) as the criterion
 

     Predictor     b      b_95%_CI sr2  sr2_95%_CI             Fit
   (Intercept) -0.18 [-0.67, 0.30]                                
 ExpConditionO -0.11 [-0.70, 0.48] .00 [-.02, .02]                
 ExpConditionV 0.78*  [0.19, 1.38] .10 [-.04, .25]                
      IfSocial -0.09 [-0.57, 0.40] .00 [-.02, .02]                
                                                        R2 = .166*
                                                   95% CI[.01,.31]
                                                                  

Note. A significant b-weight indicates the semi-partial correlation is also significant.
b represents unstandardized regression weights. 
sr2 represents the semi-partial correlation squared.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.


Gaus_cont<-lm(data = ExpDfSVOG1, Exploration~ExpCondition+PSocScore)
summary(Gaus_cont)
AIC(Gaus_cont)

Gaus_cont_scale<-lm(data = ExpDfSVOG1, scale(Exploration)~ExpCondition+scale(PSocScore))
summary(Gaus_cont_scale)
AIC(Gaus_cont_scale)

Call:
lm(formula = Exploration ~ ExpCondition + PSocScore, data = ExpDfSVOG1)

Residuals:
    Min      1Q  Median      3Q     Max 
-11.845  -3.325  -0.011   3.801  11.661 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)     20.905      1.480   14.13   <2e-16 ***
ExpConditionO   -0.608      1.681   -0.36    0.719    
ExpConditionV    4.434      1.682    2.64    0.011 *  
PSocScore       -0.166      0.182   -0.91    0.365    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.3 on 56 degrees of freedom
Multiple R-squared:  0.176,	Adjusted R-squared:  0.132 
F-statistic: 3.99 on 3 and 56 DF,  p-value: 0.012

Call:
lm(formula = scale(Exploration) ~ ExpCondition + scale(PSocScore), 
    data = ExpDfSVOG1)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.0762 -0.5829 -0.0019  0.6663  2.0441 

Coefficients:
                 Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -0.223      0.208   -1.07    0.288  
ExpConditionO      -0.107      0.295   -0.36    0.719  
ExpConditionV       0.777      0.295    2.64    0.011 *
scale(PSocScore)   -0.111      0.122   -0.91    0.365  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.93 on 56 degrees of freedom
Multiple R-squared:  0.176,	Adjusted R-squared:  0.132 
F-statistic: 3.99 on 3 and 56 DF,  p-value: 0.012


options(digits=2)
summary(Gaus_cont_scale)$coefficients


apa.reg.table(Gaus_cont_scale)


Regression results using scale(Exploration) as the criterion
 

        Predictor     b      b_95%_CI sr2  sr2_95%_CI             Fit
      (Intercept) -0.22 [-0.64, 0.19]                                
    ExpConditionO -0.11 [-0.70, 0.48] .00 [-.02, .02]                
    ExpConditionV 0.78*  [0.19, 1.37] .10 [-.04, .24]                
 scale(PSocScore) -0.11 [-0.35, 0.13] .01 [-.04, .06]                
                                                           R2 = .176*
                                                      95% CI[.01,.32]
                                                                     

Note. A significant b-weight indicates the semi-partial correlation is also significant.
b represents unstandardized regression weights. 
sr2 represents the semi-partial correlation squared.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.


Pois_additive<-glm(data = ExpDfSVOG1, Exploration~ExpCondition+IfSocial, family = poisson(link="identity"))
Pois_additive_log<-glm(data = ExpDfSVOG1, Exploration~ExpCondition+IfSocial, family = poisson(link="log"))
summary(Pois_additive)

summary(Pois_additive_log)


#apa.reg.table(Pois_additive)

Call:
glm(formula = Exploration ~ ExpCondition + IfSocial, family = poisson(link = "identity"), 
    data = ExpDfSVOG1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9173  -0.6437  -0.0827   0.8330   2.2941  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)     20.275      1.169   17.35   <2e-16 ***
ExpConditionO   -0.639      1.408   -0.45   0.6498    
ExpConditionV    4.467      1.496    2.99   0.0028 ** 
IfSocial        -0.335      1.192   -0.28   0.7788    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 91.645  on 59  degrees of freedom
Residual deviance: 77.163  on 56  degrees of freedom
AIC: 377.4

Number of Fisher Scoring iterations: 5

Call:
glm(formula = Exploration ~ ExpCondition + IfSocial, family = poisson(link = "log"), 
    data = ExpDfSVOG1)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9066  -0.6570  -0.0586   0.8465   2.2697  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)     3.0122     0.0570   52.80   <2e-16 ***
ExpConditionO  -0.0317     0.0712   -0.45   0.6559    
ExpConditionV   0.2009     0.0673    2.99   0.0028 ** 
IfSocial       -0.0231     0.0560   -0.41   0.6805    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for poisson family taken to be 1)

    Null deviance: 91.645  on 59  degrees of freedom
Residual deviance: 77.071  on 56  degrees of freedom
AIC: 377.3

Number of Fisher Scoring iterations: 4


options(digits=3)
BInomial_cat<-glm(data = ExpDfSVOG1,cbind(Exploration, 50-Exploration)~ExpCondition+IfSocial, family =binomial)


summary(BInomial_cat)

Call:
glm(formula = cbind(Exploration, 50 - Exploration) ~ ExpCondition + 
    IfSocial, family = binomial, data = ExpDfSVOG1)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-3.582  -0.838  -0.091   1.110   3.513  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)    -0.3769     0.0744   -5.07  4.0e-07 ***
ExpConditionO  -0.0524     0.0916   -0.57     0.57    
ExpConditionV   0.3631     0.0904    4.02  5.9e-05 ***
IfSocial       -0.0407     0.0744   -0.55     0.58    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 161.35  on 59  degrees of freedom
Residual deviance: 135.44  on 56  degrees of freedom
AIC: 401.1

Number of Fisher Scoring iterations: 4


options(digits=1)
summary(BInomial_cat)$coefficients


options(digits=3)
summary(BInomial_cat)$coefficients


AIC(BInomial_cat)


BInomial_cont_scale<-glm(data = ExpDfSVOG1,cbind(Exploration, 50-Exploration)~ExpCondition+scale(PSocScore), family =binomial)


summary(BInomial_cont_scale)

Call:
glm(formula = cbind(Exploration, 50 - Exploration) ~ ExpCondition + 
    scale(PSocScore), family = binomial, data = ExpDfSVOG1)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-3.454  -0.967   0.002   1.090   3.366  

Coefficients:
                 Estimate Std. Error z value Pr(>|z|)    
(Intercept)       -0.3967     0.0645   -6.15  7.8e-10 ***
ExpConditionO     -0.0510     0.0915   -0.56     0.58    
ExpConditionV      0.3599     0.0904    3.98  6.9e-05 ***
scale(PSocScore)  -0.0522     0.0374   -1.39     0.16    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 161.35  on 59  degrees of freedom
Residual deviance: 133.80  on 56  degrees of freedom
AIC: 399.4

Number of Fisher Scoring iterations: 4


options(digits=1)
summary(BInomial_cont_scale)$coefficients


options(digits=3)
summary(BInomial_cont_scale)$coefficients


AIC(BInomial_cont_scale)


BInomial_cont<-glm(data = ExpDfSVOG1,cbind(Exploration, 50-Exploration)~ExpCondition+PSocScore, family =binomial)


BInomial_cont

Call:  glm(formula = cbind(Exploration, 50 - Exploration) ~ ExpCondition + 
    PSocScore, family = binomial, data = ExpDfSVOG1)

Coefficients:
  (Intercept)  ExpConditionO  ExpConditionV      PSocScore  
      -0.3311        -0.0510         0.3599        -0.0137  

Degrees of Freedom: 59 Total (i.e. Null);  56 Residual
Null Deviance:	    161 
Residual Deviance: 134 	AIC: 399


options(digits=2)
summary(BInomial_cont)

Call:
glm(formula = cbind(Exploration, 50 - Exploration) ~ ExpCondition + 
    PSocScore, family = binomial, data = ExpDfSVOG1)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-3.454  -0.967   0.002   1.090   3.366  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)   -0.33106    0.07997   -4.14  3.5e-05 ***
ExpConditionO -0.05105    0.09153   -0.56     0.58    
ExpConditionV  0.35987    0.09044    3.98  6.9e-05 ***
PSocScore     -0.01367    0.00981   -1.39     0.16    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 161.35  on 59  degrees of freedom
Residual deviance: 133.80  on 56  degrees of freedom
AIC: 399.4

Number of Fisher Scoring iterations: 4


options(digits=1)
summary(BInomial_cont)$coefficients


options(digits=3)
summary(BInomial_cont)$coefficients


AIC(BInomial_cont)


AIC(Gaus_cont_scale,Gaus_cat_scale,BInomial_cont_scale,BInomial_cat)

	NumID	Efficiency	Generation	ExpCondition	ChainID	Age	Gender	ID
	<int>	<dbl>	<int>	<chr>	<int>	<int>	<int>	<chr>
1	1	673.64	1	I	1	20	1	I1
51	2	574.44	1	I	2	20	3	I2
101	3	690.86	1	I	3	21	3	I3
151	4	646.92	1	I	4	20	3	I4
201	5	548.62	1	I	5	20	2	I5
251	6	604.26	1	I	6	19	2	I6
301	7	696.34	1	I	7	20	3	I7
351	8	746.52	1	I	8	20	3	I8
401	9	710.72	1	I	9	21	3	I9
451	10	750.20	1	I	10	21	3	I10
501	11	677.64	1	I	11	21	3	I11
551	12	707.82	1	I	12	21	2	I12
601	13	680.68	1	I	13	21	2	I13
651	14	708.72	1	I	14	19	3	I14
701	15	736.90	1	I	15	21	3	I15
751	16	727.50	1	I	16	21	3	I16
801	17	689.64	1	I	17	20	2	I17
851	18	704.38	1	I	18	19	3	I18
901	19	677.84	1	I	19	19	3	I19
951	20	628.82	1	I	20	20	2	I20
1001	21	667.74	1	O	1	22	2	O1
1051	22	898.20	2	O	1	20	3	O1
1101	23	701.60	1	O	2	20	2	O2
1151	24	935.46	2	O	2	19	3	O2
1201	25	704.40	1	O	3	20	2	O3
1251	26	937.44	2	O	3	22	2	O3
1301	27	691.44	1	O	4	23	4	O4
1351	28	830.04	2	O	4	22	2	O4
1401	29	683.78	1	O	5	21	3	O5
1451	30	881.56	2	O	5	21	3	O5
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
3501	71	704.92	1	V	6	20	2	V6
3551	72	792.34	2	V	6	22	3	V6
3601	73	693.28	1	V	7	22	2	V7
3651	74	867.82	2	V	7	20	2	V7
3701	75	714.36	1	V	8	21	3	V8
3751	76	952.12	2	V	8	21	2	V8
3801	77	664.44	1	V	9	21	3	V9
3851	78	793.00	2	V	9	21	3	V9
3901	79	695.26	1	V	10	20	2	V10
3951	80	848.44	2	V	10	22	3	V10
4001	81	551.02	1	V	11	19	2	V11
4051	82	694.70	2	V	11	18	2	V11
4101	83	767.14	1	V	12	19	3	V12
4151	84	934.86	2	V	12	20	2	V12
4201	85	645.94	1	V	13	22	3	V13
4251	86	936.38	2	V	13	19	3	V13
4301	87	733.98	1	V	14	20	3	V14
4351	88	914.00	2	V	14	19	3	V14
4401	89	766.42	1	V	15	21	3	V15
4451	90	975.00	2	V	15	19	3	V15
4501	91	628.82	1	V	16	19	3	V16
4551	92	934.64	2	V	16	20	3	V16
4601	93	723.02	1	V	17	20	2	V17
4651	94	967.10	2	V	17	20	3	V17
4701	95	658.92	1	V	18	19	2	V18
4751	96	880.12	2	V	18	19	2	V18
4801	97	699.54	1	V	19	19	2	V19
4851	98	901.94	2	V	19	19	2	V19
4901	99	636.30	1	V	20	20	3	V20
4951	100	825.40	2	V	20	22	2	V20

	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
X1	1	20	20.1	3.51	19	20	3.71	15	27	12	0.34	-1.11	0.78

	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
X1	1	20	19.45	5.56	20	19.81	5.93	8	27	19	-0.41	-0.99	1.24

	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
X1	1	20	24.6	6.43	24	24.62	5.19	12	37	25	-0.07	-0.53	1.44

	vars	n	mean	sd	median	trimmed	mad	min	max	range	skew	kurtosis	se
	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>	<dbl>
X1	1	20	11.15	5.32	10	11.12	5.93	1	21	20	0.06	-1.08	1.19

	Estimate	Std. Error	t value	Pr(>\|t\|)
(Intercept)	-0.182	0.24	-0.75	0.456
ExpConditionO	-0.110	0.30	-0.37	0.713
ExpConditionV	0.784	0.30	2.64	0.011
IfSocial	-0.086	0.24	-0.36	0.723

	Estimate	Std. Error	t value	Pr(>\|t\|)
(Intercept)	-0.22	0.21	-1.07	0.288
ExpConditionO	-0.11	0.29	-0.36	0.719
ExpConditionV	0.78	0.29	2.64	0.011
scale(PSocScore)	-0.11	0.12	-0.91	0.365

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	-0.38	0.07	-5.1	4e-07
ExpConditionO	-0.05	0.09	-0.6	6e-01
ExpConditionV	0.36	0.09	4.0	6e-05
IfSocial	-0.04	0.07	-0.5	6e-01

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	-0.3769	0.0744	-5.067	4.05e-07
ExpConditionO	-0.0524	0.0916	-0.572	5.67e-01
ExpConditionV	0.3631	0.0904	4.016	5.91e-05
IfSocial	-0.0407	0.0744	-0.547	5.85e-01

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	-0.40	0.06	-6.1	8e-10
ExpConditionO	-0.05	0.09	-0.6	6e-01
ExpConditionV	0.36	0.09	4.0	7e-05
scale(PSocScore)	-0.05	0.04	-1.4	2e-01

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	-0.3967	0.0645	-6.149	7.81e-10
ExpConditionO	-0.0510	0.0915	-0.558	5.77e-01
ExpConditionV	0.3599	0.0904	3.979	6.92e-05
scale(PSocScore)	-0.0522	0.0374	-1.393	1.64e-01

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	-0.33	0.08	-4.1	3e-05
ExpConditionO	-0.05	0.09	-0.6	6e-01
ExpConditionV	0.36	0.09	4.0	7e-05
PSocScore	-0.01	0.01	-1.4	2e-01

	Estimate	Std. Error	z value	Pr(>\|z\|)
(Intercept)	-0.3311	0.07997	-4.140	3.48e-05
ExpConditionO	-0.0510	0.09153	-0.558	5.77e-01
ExpConditionV	0.3599	0.09044	3.979	6.92e-05
PSocScore	-0.0137	0.00981	-1.393	1.64e-01

	df	AIC
	<dbl>	<dbl>
Gaus_cont_scale	5	168
Gaus_cat_scale	5	168
BInomial_cont_scale	4	399
BInomial_cat	4	401

A data.frame: 6 × 15
	ID	ChainID	Generation	ExpCondition	Age	Gender	Trial	Choice	Length	Width	Thickness	Fitness	erroredFitness	EarnedMoney	TimeStamp
	<chr>	<int>	<int>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<dbl>	<dbl>
1	I1	1	1	I	20	1	1	1	14	6	83	364	373	14.92	2.022012e+13
2	I1	1	1	I	20	1	2	2	19	6	83	394	373	14.92	2.022012e+13
3	I1	1	1	I	20	1	3	1	19	6	83	394	386	30.36	2.022012e+13
4	I1	1	1	I	20	1	4	2	24	6	83	421	386	30.36	2.022012e+13
5	I1	1	1	I	20	1	5	1	24	6	83	421	413	46.88	2.022012e+13
6	I1	1	1	I	20	1	6	2	29	6	83	446	413	46.88	2.022012e+13

A data.frame: 5000 × 17
ID	ChainID	Generation	ExpCondition	Age	Gender	Trial	Choice	Length	Width	Thickness	Fitness	erroredFitness	EarnedMoney	TimeStamp	NumID	Order
<chr>	<int>	<int>	<chr>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<int>	<dbl>	<dbl>	<int>	<dbl>
I1	1	1	I	20	1	1	1	14	6	83	364	373	14.92	2.022012e+13	1	16
I1	1	1	I	20	1	2	2	19	6	83	394	373	14.92	2.022012e+13	1	16
I1	1	1	I	20	1	3	1	19	6	83	394	386	30.36	2.022012e+13	1	16
I1	1	1	I	20	1	4	2	24	6	83	421	386	30.36	2.022012e+13	1	16
I1	1	1	I	20	1	5	1	24	6	83	421	413	46.88	2.022012e+13	1	16
I1	1	1	I	20	1	6	2	29	6	83	446	413	46.88	2.022012e+13	1	16
I1	1	1	I	20	1	7	1	29	6	83	446	446	64.72	2.022012e+13	1	16
I1	1	1	I	20	1	8	2	34	6	83	467	446	64.72	2.022012e+13	1	16
I1	1	1	I	20	1	9	1	34	6	83	467	466	83.36	2.022012e+13	1	16
I1	1	1	I	20	1	10	2	34	6	78	497	466	83.36	2.022012e+13	1	16
I1	1	1	I	20	1	11	1	34	6	78	497	502	103.44	2.022012e+13	1	16
I1	1	1	I	20	1	12	2	34	6	73	525	502	103.44	2.022012e+13	1	16
I1	1	1	I	20	1	13	1	34	6	73	525	516	124.08	2.022012e+13	1	16
I1	1	1	I	20	1	14	2	34	6	68	551	516	124.08	2.022012e+13	1	16
I1	1	1	I	20	1	15	1	34	6	68	551	549	146.04	2.022012e+13	1	16
I1	1	1	I	20	1	16	2	34	6	63	574	549	146.04	2.022012e+13	1	16
I1	1	1	I	20	1	17	1	34	6	63	574	579	169.20	2.022012e+13	1	16
I1	1	1	I	20	1	18	2	34	6	58	596	579	169.20	2.022012e+13	1	16
I1	1	1	I	20	1	19	1	34	6	58	596	598	193.12	2.022012e+13	1	16
I1	1	1	I	20	1	20	2	34	6	53	615	598	193.12	2.022012e+13	1	16
I1	1	1	I	20	1	21	1	34	6	53	615	615	217.72	2.022012e+13	1	16
I1	1	1	I	20	1	22	2	34	6	48	632	615	217.72	2.022012e+13	1	16
I1	1	1	I	20	1	23	1	34	6	48	632	623	242.64	2.022012e+13	1	16
I1	1	1	I	20	1	24	2	39	6	48	651	623	242.64	2.022012e+13	1	16
I1	1	1	I	20	1	25	1	39	6	48	651	639	268.20	2.022012e+13	1	16
I1	1	1	I	20	1	26	2	39	11	48	704	639	268.20	2.022012e+13	1	16
I1	1	1	I	20	1	27	1	39	11	48	704	710	296.60	2.022012e+13	1	16
I1	1	1	I	20	1	28	1	39	11	48	704	716	325.24	2.022012e+13	1	16
I1	1	1	I	20	1	29	2	39	16	48	750	716	325.24	2.022012e+13	1	16
I1	1	1	I	20	1	30	1	39	16	48	750	745	355.04	2.022012e+13	1	16
⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮	⋮
V20	20	2	V	22	2	21	2	70	46	73	833	809	325.20	2.022021e+13	100	8
V20	20	2	V	22	2	22	1	70	46	73	833	840	358.80	2.022021e+13	100	8
V20	20	2	V	22	2	23	1	70	46	73	833	835	392.20	2.022021e+13	100	8
V20	20	2	V	22	2	24	2	70	41	73	827	835	392.20	2.022021e+13	100	8
V20	20	2	V	22	2	25	1	70	41	73	827	821	425.04	2.022021e+13	100	8
V20	20	2	V	22	2	26	2	70	43	73	830	821	425.04	2.022021e+13	100	8
V20	20	2	V	22	2	27	1	70	43	73	830	817	457.72	2.022021e+13	100	8
V20	20	2	V	22	2	28	1	70	43	73	830	820	490.52	2.022021e+13	100	8

A data.frame: 2 × 3
	asocial	unrepaid	repaid
	<int>	<int>	<int>
G1	3	3	11
G2	0	14	15

A data.frame: 2 × 3
	asocial	unrepaid	repaid
	<dbl>	<dbl>	<dbl>
G1	0.15	0.15	0.55
G2	0.00	0.70	0.75

Table of Contents

Preparation¶

Loading packages¶

Execution environment¶

Reading data¶

Set Numerical ID for each participants (from 1 to 100)¶

Color coordination¶

Making Summary Efficiency¶

Exploration¶

Making Summary Data¶

Visualizing exploration and exploration for each participant¶

Making Summary Dataset¶

Visualizing Summary¶

Bar graphs + Cloud Plots¶

Bar Graph¶

t-tests¶

Preparation¶

Nonrepaid(OBL) vs. Asocial(IND)¶

Repaid(VER) vs. Asocial(IND)¶

Second Generation: OBL(unrepaid) vs. VER(repaid)¶

Parameter Shifts¶

Counting Over Exploration¶

Length¶

Width¶

Thickness¶

Efficiency¶

Visualization in whole trials¶

Mean Efficiency (pointless graph)¶

(Visualization)¶

FInal trial comparison¶

Repaid vs. Asocial¶

Nonrepaid vs. Asocial¶

Second Generation¶

(Cumulative Cultural Evolution)¶

Questionnaire¶

Strategy Proportion¶

(Other scales)¶

SVO¶

Coding SVO¶

SVO Categorization¶

SVO summary¶

By Three Category C:Prosocial, D: Defector (Proself+Competitor), N:non-classified¶

Whether prosicial (1) or not (0)¶

SVO + LM/GLM¶

Data Merge¶

Gaussian¶

SVO as categorical¶

Exploration is scaled¶

SVO as contnious¶

(Poisson)¶

Binomial¶

SVO as categorical¶

SVO as continuous¶

Items are scaled¶

Items are not scaled¶